AI container orchestration platform for everyone
dstack is an open-source orchestration platform for development, training, and deployment of AI models in any cloud or data center.
Dev environments
Before scheduling a task or deploying a model, you may want to run code interactively.
Dev environments allow you to provision a remote machine set up with your code and favorite IDE with just one command.
Tasks
Tasks allow for convenient scheduling of various batch jobs, such as training, fine-tuning, or data processing, as well as running web applications.
You can run tasks on a single machine or on a cluster of nodes.
Services
Services make it very easy to deploy any kind of model as public, secure, and scalable endpoints.
Why community dstack
Andrew Spott
ML Engineer at Stealth Startup
Thanks to @dstack, I get the convenience of having a personal Slurm cluster and using budget-friendly cloud GPUs, without paying the super-high premiums charged by the big three.
Alvaro Bartolome
ML Engineer at Argilla
With @dstack it's incredibly easy to define a configuration within a repository and run it without worrying about GPU availability. It lets you focus on data and your research.
Park Chansung
ML Researcher at ETRI
Thanks to @dstack, I can effortlessly access the top GPU options across different clouds, saving me time and money while pushing my AI work forward.
Eckart Burgwedel
CEO at Uberchord
With @dstack, running an open-source LLM or a dev environment on a cloud GPU is as easy as running a local Docker container. It combines the ease of Docker with the auto-scaling capabilities of K8s.
Peter Hill
Co-Founder at CUDO Compute
@dstack is instrumental in simplifying infrastructure provisioning and AI model development. if your organization is on the lookout for an platform to speed up the adoption of AI, I wholeheartedly recommend @dstack
Examples
Llama 3
Deploy Llama 3 as an endpoint using Ollama.
Alignment Handbook
Fine-tune Gemma 7B on a custom dataset.
vLLM
Deploy LLMs as endpoints using vLLM.
Axolotl
Fine-tune Llama 3 on a custom dataset using Axolotl.
TGI
Deploy LLMs as endpoints using TGI.
Ollama
Deploy LLMs as endpoints using Ollama.
LoRaX
Deploy thousands of models on a single endpoint using LoRaX.
More
Find more examples on training and deploying models.
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FAQ
dstack is an open-source orchestration platform for efficiently training and deploying AI models across any cloud or data center. It provides a unified interface to manage AI model development at any scale, whether in the cloud or on-premises.
With dstack, you can utilize various cloud providers or on-prem infrastructure, along with any hardware, and leverage open-source frameworks and tools for both training and deployment.
dstack Sky is a managed version of dstack. Unlike the open-source dstack where you have to set up and manage the server yourself, dstack Sky takes care of hosting the server on your behalf. Most importantly, dstack Sky offers access to cloud GPUs through its marketplace, ensuring you get them at competitive rates.
If needed, you can still configure dstack Sky to use your own cloud accounts or connect to your on-prem servers.
The open-source version of dstack is completely free for commercial use. You can use it with your own cloud accounts or data centers without any costs, and you can also rely on support from the open-source community, Discord, and other channels.
If you use cloud GPUs through dstack Sky, you're only charged based on the compute prices offered by the marketplace provider. dstack Sky does not add any commission on top of these prices.
If you need enterprise-grade SLAs, premium support, or additional features, please reach out to our team.